import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_excel("AirQuality.xlsx")
data.head(10)
del data['lastupdate']
data.head()
plt.plot(data['Avg'])
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('Average Pollution Data')
plt.plot(data['Max'])
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('Maximum Pollution Data')
plt.plot(data['Min'])
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('Minimum Pollution Data')
plt.figure(figsize=(20,10), dpi = 80)
sns.countplot(x='State',data=data)
plt.xlabel('State')
plt.tight_layout()
data_p1=data[data.Pollutants=='PM2.5']
data_p1[['Max','Avg','Min']].plot()
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('PM2.5')
data_p2=data[data.Pollutants=='PM10']
data_p2[['Max','Avg','Min']].plot()
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('PM10')
data_p3=data[data.Pollutants=='NO2']
data_p3[['Max','Avg','Min']].plot()
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('NO2')
data_p4=data[data.Pollutants=='NH3']
data_p4[['Max','Avg','Min']].plot()
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('NH3')
data_p5=data[data.Pollutants=='SO2']
data_p5[['Max','Avg','Min']].plot()
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('SO2')
data_p6=data[data.Pollutants=='CO']
data_p6[['Max','Avg','Min']].plot()
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('CO')
data_p7=data[data.Pollutants=='OZONE']
data_p7[['Max','Avg','Min']].plot()
plt.xlabel('cities')
plt.ylabel('amount')
plt.title('OZONE')
from pandas import DataFrame
df =DataFrame(data.State)
DataFrame.drop_duplicates(df)
data_state1=data[data.State=='Andhra_Pradesh']
data_state1[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Andhra_Pradesh')
data_state2=data[data.State=='Bihar']
data_state2[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Bihar')
data_state3=data[data.State=='Delhi']
data_state3[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Delhi')
data_state4=data[data.State=='Gujarat']
data_state4[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Gujarat')
data_state5=data[data.State=='Haryana']
data_state5[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Haryana')
data_state6=data[data.State=='Jharkhand']
data_state6[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Jharkhand')
data_state7=data[data.State=='Karnataka']
data_state7[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Karnataka')
data_state8=data[data.State=='Kerala']
data_state8[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Kerala')
data_state9=data[data.State=='Madhya Pradesh']
data_state9[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Madhya Pradesh')
data_state10=data[data.State=='Maharashtra']
data_state10[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Maharashtra')
data_state11=data[data.State=='Odisha']
data_state11[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Odisha')
data_state12=data[data.State=='Punjab']
data_state12[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Punjab')
data_state13=data[data.State=='Rajasthan']
data_state13[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Rajasthan')
data_state14=data[data.State=='TamilNadu']
data_state14[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('TamilNadu')
data_state15=data[data.State=='Telangana']
data_state15[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Telangana')
data_state16=data[data.State=='Uttar_Pradesh']
data_state16[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('Uttar_Pradesh')
data_state17=data[data.State=='West_Bengal']
data_state17[['Min','Avg','Max']].plot()
plt.xlabel('cities')
plt.ylabel('Amount')
plt.title('West_Bengal')
data_pollu=data.groupby('Pollutants')
data_pollu.mean()
plt.figure(figsize=(20,10) , dpi=100)
plt.plot(data_pollu.mean())
plt.legend(['Max','Avg','Mean'])
plt.xlabel('Amount')
plt.ylabel('Pollutant Amounts')
data_states=data.groupby('State')
data_states.mean()
plt.figure(figsize=(18,5) , dpi=100)
plt.plot(data_states.mean())
plt.legend(['Max','Avg','Min'])
plt.xlabel('States')
plt.ylabel('Amount')
plt.tight_layout()
data_city=data.groupby('city')
data_city.mean()
plt.figure(figsize=(150,50) , dpi=100)
plt.plot(data_city.mean())
plt.xlabel('City')
plt.ylabel('Amount')
plt.show()